The heart relies on an organized sequence of electrical impulses in order to beat effectively. Any deviation from this normal sequence is known as “arrhythmia.” A class of devices includes signal processing software that analyzes electrocardiography (ECG) signals acquired from the victim to determine when a cardiac arrhythmia such as ventricular fibrillation (VF) or shockable ventricular tachycardia (VT) exists. These devices include automated external defibrillators (AEDs), ECG rhythm classifiers, or ventricular arrhythmia detectors. An AED is a device that literally “talks” the provider through a process of evaluating a patient for, attaching the patient to, and activating, the AED therapy. This device is capable of recognizing the two distinct cardiac waveforms: VT and VF.
VT is a tachydysrhythmia originating from a ventricular ectopic focus, characterized by a rate typically greater than 120 beats per minute and wide QRS complexes. VT may be monomorphic (typically regular rhythm originating from a single focus with identical QRS complexes) or polymorphic (unstable, may be irregular rhythm, with varying QRS complexes). An example rhythm for an unstable VT is illustrated in FIG. 1A. Depending on the rate and the length of time that the VT has been sustained, a heart in the VT state may or may not produce a pulse (i.e., pulsatile movement of blood through the circulatory system). The cardiac activity still has some sense of organization (note that the “loops” are all basically the same size and shape). If there is no pulse associated with this VT rhythm, then the VT is considered to be unstable and a life threatening condition. An unstable VT can be treated with an electrical shock or defibrillation.
Supraventricular tachycardia (SVT) is a rapid heartbeat that begins above the hearts lower chambers (the ventricles). SVT is an abnormally fast heart rhythm that begins in one of the upper chambers of the heart (atria), a component of the heart's electrical conduction system called the atrioventricular (AV) node, or both. Although SVT is rarely life-threatening, the symptoms which include a feeling of a racing heart, fluttering or pounding in the chest or extra heartbeats (palpitations), or dizziness can be uncomfortable.
VF is usually an immediate life threat. VF is a pulseless arrhythmia with irregular and chaotic electrical activity and ventricular contraction in which the heart immediately loses its ability to function as a pump. VF is the primary cause of sudden cardiac death (SCD). An example rhythm for VF is illustrated in FIG. 1B. This waveform does not have a pulse associated with it. There is no organization to this rhythm (note the irregular size and shape of the loops.) The pumping part of the heart is quivering like a bag of worms, and it is highly unlikely that this activity will move any blood. The corrective action for this rhythm is to defibrillate the heart using an electrical charge.
A normal heart beat wave starts at the sinoatrial node (SA node) and progresses toward the far lower corner of the left ventricle.
A massive electrical shock to the heart can correct the VF and unstable VT rhythms. This massive electrical shock can force all the cardiac cells in the heart to depolarize at the same time. Subsequently, all of the cardiac cells go into a short resting period. The hope is that the sinoatrial node (SA node) will recover from this shock before any of the other cells, and that the resulting rhythm will be a pulse producing rhythm if not normal sinus rhythm.
For AEDs, algorithms to recognize the two waveforms VT and VF are designed to perform ECG analyses at specific times during a rescue event of a patient using defibrillation and cardio-pulmonary resuscitation (CPR). The first ECG analysis is usually initiated within a few seconds following attachment of the defibrillation electrodes to the patient. Subsequent ECG analyses may or may not be initiated based upon the results of the first analysis. Typically, if the first analysis detects a shockable rhythm, the rescuer is advised to deliver a defibrillation shock. Following the shock delivery, a second analysis is automatically initiated to determine whether the defibrillation treatment was successful or not (i.e., the shockable ECG rhythm has been converted to a normal or other non-shockable rhythm). If this second analysis detects the continuing presence of a shockable arrhythmia, the AED advises the user to deliver a second defibrillation treatment. A third ECG analysis may then be initiated to determine whether the second shock was or was not effective. If a shockable rhythm persists, the rescuer is then advised to deliver a third defibrillation treatment.
Following the third defibrillator shock or when any of the analyses described above detects a non-shockable rhythm, treatment protocols recommended by the American Heart Association and European Resuscitation Council require the rescuer to check the patient's pulse or to evaluate the patient for signs of circulation. If no pulse or signs of circulation are present, the rescuer is trained to perform CPR on the victim for a period of one or more minutes. The CPR includes rescue breathing and chest compressions. Following this period of CPR, the AED reinitiates a series of up to three additional ECG analyses interspersed with appropriate defibrillation treatments as described above. The sequence of three ECG analyses/defibrillation shocks followed by 1-3 minutes of CPR, continues in a repetitive fashion for as long as the AED's power is turned on and the patient is connected to the AED device. Typically, the AED provides audio prompts to inform the rescuer when analyses are about to begin, what the analysis results were, and when to start and stop the delivery of CPR.
One limitation associated with many AEDs is that current automated ECG rhythm analysis methods cannot function with extra noise due to CPR chest compressions. Thus, conventional practice is to interrupt chest compressions while performing ECG rhythm analysis. Long interruptions of chest compressions have been shown to result in higher failure rate of resuscitation. Many studies have reported that the discontinuation of precordial compression can significantly reduce the recovery rate of spontaneous circulation and 24-hour survival rate. These studies include “Adverse effects of interrupting precordial compression during cardio-pulmonary resuscitation” by Sato et al. (Critical Care Medicine, Volume 25(5), May 1997, pp 733-736), “Adverse Outcomes of Interrupted Precordial Compression During Automated Defibrillation” by Yu et al. (Circulation, 2002), and “Predicting Outcome of Defibrillation by Spectral Characterization and Nonparametric Classification of Ventricular Fibrillation in Patients With Out-of-Hospital Cardiac Arrest” by Eftestøl et al. (Circulation, 2002). Thus, it is useful to recognize abnormal heart rhythms during chest compressions.
There is recent clinical evidence showing that performing chest compressions prior to defibrillation under some circumstances can be beneficial. Specifically, it is clinically beneficial to treat a patient with chest compressions prior to defibrillation if the response times of the medical emergency system result in a delay of more than four minutes such that the patient is in cardiac arrest for more than four minutes. If the response times of the medical emergency system result in a capability to treat the patient in sooner than a four minute delay, it can be better for the patient to be treated with defibrillation first. Methods have been developed to determine from the ECG waveform both whether the patient has been in cardiac arrest for longer than the 4 minutes as well as time independent measures of when the most optimal time is to shock. “Non-invasive monitoring and treatment of subjects in cardiac arrest using ECG parameters predictive of outcome” by Brown and Dzwonczyk (U.S. Pat. No. 5,683,424) describes methods to determine from the ECG waveform whether the patient has been in cardiac arrest for longer than the 4 minutes. “Method and system for predicting the immediate success of a defibrillatory shock during cardiac arrest” (U.S. Pat. No. 6,171,257 by Weil et al.) and “Ventricular Fibrillation Scaling Exponent Can Guide Timing of Defibrillation and Other Therapies” by Menegazzi et al. (2004 American Heart Association, Inc.) describe time independent measures of when the most optimal time is to shock. These algorithms use spectral analysis of the ECG to predict defibrillation shock success in some manner. Current methods utilizing spectral analysis of the ECG for chest compression artifact rejection, defibrillation success prediction, and therapeutic decision-making typically specify a set of parameters in the ECG frequency spectrum to be detected. For example, U.S. Pat. No. 5,683,424 compares a centroid or a median frequency or a peak power frequency from a calculated frequency spectrum of the ECG to thresholds to determine if a defibrillating shock is necessary. These parameters do not uniquely specify the frequency or time domain characteristics. For example, the median frequency of the ECG spectrum for almost all patients in ventricular fibrillation decreases initially then increases again after several minutes, making it difficult to use median frequency to predict how long a patient has been in cardiac arrest. Thus, the patient can have the same median frequency at widely differing durations of cardiac arrest. Using amplitudes of the frequency spectrum of the ECG can be limited because the amplitudes are dependent on both the cardiac electrical output as well as position of the ECG lead electrodes on the patient.
Some conventional automated ECG rhythm analysis methods detect VF and other arrhythmic heart rhythms by using spectral analysis of the ECG signals with the assumption that the difference in the power spectrum between ECGs of normal heart rhythms and abnormal rhythms is such that during the abnormal rhythm the ECG is concentrated or mainly sinusoidal in a narrow band of frequencies between 4 and 7 Hz, while in normal rhythm the ECG is a broadband signal with major harmonics up to at least 25 Hz. For example, “Comparison of four techniques for recognition of ventricular fibrillation from the surface” by Clayton et al. (ECG Medical & Biological Engineering & Computing 1993; 31:111-117) and “Algorithmic sequential decision-making in the frequency domain for life threatening ventricular arrhythmias and imitative artifacts: a diagnostic system” by Barro et al. (Journal of Biomedical Engineering, 1989, Volume 11) analyze the frequency domain of the ECG to check if the ECG is mainly sinusoidal in the narrow band of frequencies. One problem with these conventional methods is that CPR changes the assumption behind the methods so that VF and other dangerous rhythms cannot be typically detected during chest compressions.
Adaptive filters have been used in many studies to remove the artifact due to CPR chest compression from the ECG signal. These studies include “CPR Artifact Removal from Human ECG Using Optimal Multichannel Filtering “by Aase et al. (IEEE Transactions on Biomedical Engineering, Vol. 47, No. 11, November 2000), “Removal of Cardiopulmonary Resuscitation Artifacts From Human ECG Using an Efficient Matching Pursuit-Like Algorithm” by Husøy et al. (IEEE Transactions on Biomedical Engineering, Vol. 49, No. 11, November 2002), “and U.S. Pat. No. 6,390,996 by Halperin et al (2002). The adaptive filters use compression depth and thoracic impedance as reference signals to estimate the artifacts in the ECG signal. The adaptive filter's parameters are updated by calculating the inverse of a cross-correlation matrix or the auto- and cross-spectra of the signal. The artifacts could be reduced when these adaptive filters were applied. However, there is usually a significant part of the artifact left in the estimated ECG signal. Moreover, the adaptive-filter algorithm sometimes has a high computational complexity.
These adaptive filtering methods use the compression depth as the reference signal to remove the chest compression artifact from the ECG signals. This is based on the assumption that the chest compression artifact is correlated with the reference signal (compression depth) and independent of the desired ECG signal. This can be true for an infinitely long ECG signal but the estimated coefficients can be biased if a limited length of the ECG signal is applied. It is also possible that the reference signals (such as the compression depth) can provide only part of the information about the CPR artifact presented in the ECG signal, i.e. the noise-reduction ability of the adaptive filter is limited by its knowledge of the noise. Fitzgibbon et al. in “Determination of the noise source in the electrocardiogram during cardiopulmonary resuscitation” (Critical Care Medicine 2002 Vol. 30, No. 4) reported that the thoracic impedance variation due to ventilation or chest compression has little correlation with the artifact in ECG recording during chest compressions. Fitzgibbon et al. (2002) further suggested that the source of the noise in the signal during chest compressions is the electrode motion and related to the electrode's electrical properties, which makes the relation between the noise and the compression depth more complicated. Thus, the artifact cannot be sufficiently attenuated for satisfactory results with the conventional advisory algorithm for fibrillation detection.
One method for evaluating medical tests is to determine a test's ability to correctly detect disease, also known as sensitivity, and the test's ability to avoid labeling normal things as disease, also known as specificity. Ideally, a medical test has 100% sensitivity and 100% specificity. When a medical test is imperfect, sensitivity and specificity are plotted on a graph called a receiver-operator characteristics (ROC) curve. Variables in the medical test can be chosen such that the resulting point of the medical test on the ROC curve is closest to a point with 100% sensitivity and 100% specificity.